Dynamic report

# The `params` object is available in the document.

data_acc_sps_prov <- data |> 
  dplyr::filter(species_code == params$species_code,
                subnational1_code %in% params$provinces) |> # User input
  dplyr::mutate(yearmon = as.yearmon(as.Date(date))) |> 
  dplyr::group_by(yearmon, subnational1_code) |> 
  dplyr::summarize(n = dplyr::n()) |> 
  ungroup() |> 
  group_by(subnational1_code) |> 
  dplyr::mutate(cumsum = cumsum(n))

## `summarise()` has grouped output by 'yearmon'. You can override using the
## `.groups` argument.

## LINEPLOT
    plotly::ggplotly(
      data_acc_sps_prov  |> 
      ggplot2::ggplot(aes(x = as.Date(yearmon),
                          y = cumsum,
                          color = subnational1_code)) +
      ggplot2::geom_line(size = 0.5,
                         alpha = 0.5) +
      ggplot2::geom_point(alpha = 0.5) +
      ggplot2::scale_x_date(date_breaks = "1 month", 
                            date_labels =  "%b %Y") + 
      #   ggplot2::scale_color_brewer(palette = "Set2") +
      ggplot2::labs(title = paste('Period', min(data$date),
                                  "to",
                                  max(data$date)),
                    x = 'date',
                    y = 'observations') 
  )

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.

## BOXPLOT
 data |> 
  dplyr::filter(species_code == params$species_code,
                subnational1_code %in% params$provinces) |> # User input
  dplyr::mutate(yearmon = as.yearmon(as.Date(date))) |> 
  mutate(month = factor(format(yearmon, "%b"),
                        levels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun",
                                   "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))) |> 
  pivot_longer(starts_with("fed_in"),
               values_to = 'fed_in_val' ,
               names_to= 'fed_in_names' ) |> 
  ggplot(aes(month, how_many)) +
  geom_boxplot()+
  geom_jitter(aes(color = subnational1_code))